Extracting higher-conductivity designs for solid polymer electrolytes by quantum-inspired annealing

Area of Science:

• Materials Science
• Computational Chemistry
• Energy Storage

Background:

• Data-driven exploration for energy materials faces challenges in prediction accuracy and vast search spaces.
• Optimizing material structures for energy devices requires efficient methods to overcome these limitations.

Purpose of the Study:

• To develop and validate a data trend analysis system for accelerated materials discovery using quantum-inspired annealing.
• To enhance the prediction accuracy of structure-property relationships for energy materials.
• To explore optimal material candidates for solid-state lithium-ion batteries.

Main Methods:

• Utilized a hybrid decision tree and quadratic regression algorithm to learn structure-property relationships.
• Employed quantum-inspired annealing on Fujitsu Digital Annealer hardware for rapid exploration of promising material structures.
• Investigated the system's efficacy through experimental studies on solid polymer electrolytes.

Main Results:

• Developed a novel trithiocarbonate polymer electrolyte.
• Achieved ionic conductivity of 10^-6 S cm^-1 at room temperature for a glassy polymer electrolyte.
• Demonstrated the system's capability to accelerate the discovery of functional materials.

Conclusions:

• The proposed data trend analysis system effectively accelerates the exploration of functional materials for energy applications.
• Quantum-inspired annealing provides a powerful approach for navigating large materials search spaces.
• Data-driven molecular design is crucial for advancing energy storage technologies.